Titlebook: Neural Information Processing; 27th International C Haiqin Yang,Kitsuchart Pasupa,Irwin King Conference proceedings 2020 Springer Nature Sw

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书目名称Neural Information Processing影响因子(影响力)




书目名称Neural Information Processing影响因子(影响力)学科排名




书目名称Neural Information Processing网络公开度




书目名称Neural Information Processing网络公开度学科排名




书目名称Neural Information Processing被引频次




书目名称Neural Information Processing被引频次学科排名




书目名称Neural Information Processing年度引用




书目名称Neural Information Processing年度引用学科排名




书目名称Neural Information Processing读者反馈




书目名称Neural Information Processing读者反馈学科排名

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Data Mining ENCODE Data Predicts a Significant Role of SINA3 in Human Liver Canceron we compared histone modification between HepG2 and HepG2.2.15 cells (HepG2 derived hepatitis B virus (HBV) expressing stable cells) and observed an increase SIN3A enrichment in promoter regions (H3K4me3) confirming a known biological phenotype. The mechanistic role of SIN3A protein in case of liv

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Enhancer-DSNet: A Supervisedly Prepared Enriched Sequence Representation for the Identification of Er-DSNet methodology is evaluated on a publicly available benchmark dataset and independent test set. Experimental results over benchmark independent test set indicate that proposed Enhancer-DSNet methodology outshines the performance of most recent predictor by the figure of 2%, 1%, 2%, and 5% in te

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Temporal-Lobe

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Phase Synchronization Indices for Classification of Action Intention Understanding Based on EEG Signuency band. We conclude that the phase synchronization indices are extremely useful for the classification task, the sum of significant edge values is an effective classification feature, and the action intention understanding closely correlates with the alpha frequency band.

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Are Deep Neural Architectures Losing Information? Invertibility is Indispensableoration tasks: image denoising, JPEG image decompression and image inpainting. Experimental results show that IRAE consistently outperforms non-invertible ones. Our model even contains far fewer parameters. Thus, it may be worthwhile to try replacing standard components of deep neural architectures

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